Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 1; p. 487
• Main Authors: Piłat-Rożek, Magdalena, Łazuka, Ewa, Majerek, Dariusz, Szeląg, Bartosz, Duda-Saternus, Sylwia, Łagód, Grzegorz
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2023; MDPI
• Subjects: Arrays; Carbon; Chemical compounds; Chemical fingerprinting; Chemical oxygen demand; Classification; Data analysis; Discriminant analysis; electronic nose; Electronic noses; Environmental impact; Evaluation; Gas sensors; k-median method; Laboratories; Machine learning; Methods; Multidimensional data; Multidimensional methods; multidimensional signals analysis; Purification; random forest; Sensor arrays; Sensors; Sewage; Sludge; Supervised learning; Surface water; t-SNE method; Wastewater treatment; Water treatment; Poland; k-median method; wastewater treatment; multidimensional signals analysis; gas sensors array; electronic nose; t-SNE method; random forest; machine learning; wastewater quality
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23010487

The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data. It showed that observations from different stages of the wastewater treatment process have varying chemical fingerprints. In the final stage of data analysis, a supervised machine learning method, in the form of a random forest, was used to classify observations based on the measurements from the sensors array. The quality of the resulting model was assessed based on several measures commonly used in classification tasks. All the measures used confirmed that the classification model perfectly assigned classes to the observations from the test set, which also confirmed the absence of model overfitting.